More by Mark Friedl

Abstract

We present a new approach to factor rotation for functional data.
This is achieved by rotating the functional principal components
toward a predefined space of periodic functions designed to
decompose the total variation into components that are
nearly-periodic and nearly-aperiodic with a predefined period.
We show that the factor rotation can be obtained by calculation
of canonical correlations between appropriate spaces which make
the methodology computationally efficient. Moreover, we
demonstrate that our proposed rotations provide stable and
interpretable results in the presence of highly complex
covariance. This work is motivated by the goal of finding
interpretable sources of variability in gridded time series of
vegetation index measurements obtained from remote sensing, and
we demonstrate our methodology through an application of factor
rotation of this data.

Supplemental materials

Supplementary material: Description of data and details of
simulation. The supplementary material is divided into 3
sections. The first section provides a detailed
description of the Harvard Forest data that is used in
this article, including preprocessing steps. We also
provide a detailed description of the imputation steps for
pixels with missing observations. The second section
provides a description of Annual Information and its
application is demonstrated through a simulation study.
The last section provides results related to the bootstrap
hypothesis testing procedure proposed in this article. In
particular, we present the test results on the Harvard
Forest data and simulation studies where we explore the
empirical power curve and size on simulated data
sets.